{"total":26,"rows":[{"id":"2106.04350","title":"Nonsmooth Implicit Differentiation for Machine Learning and Optimization","created":"2021-06-08","cited_by_count":43,"area":null},{"id":"2405.18437","title":"Transductive Zero-Shot and Few-Shot CLIP","created":"2024-04-08","cited_by_count":32,"area":null},{"id":"2111.12506","title":"Generalized Normalizing Flows via Markov Chains","created":"2021-11-24","cited_by_count":23,"area":null},{"id":"2107.14742","title":"Connections between Numerical Algorithms for PDEs and Neural Networks","created":"2021-07-30","cited_by_count":10,"area":null},{"id":"2208.08772","title":"Lifted Bregman Training of Neural Networks","created":"2022-08-18","cited_by_count":10,"area":null},{"id":"2306.17332","title":"Designing Stable Neural Networks using Convex Analysis and ODEs","created":"2023-06-29","cited_by_count":10,"area":null},{"id":"2210.14545","title":"Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference","created":"2022-10-26","cited_by_count":9,"area":null},{"id":"2303.11835","title":"Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian","created":"2023-03-20","cited_by_count":9,"area":null},{"id":"2302.10287","title":"CertViT: Certified Robustness of Pre-Trained Vision Transformers","created":"2023-02-01","cited_by_count":6,"area":null},{"id":"2106.16239","title":"Fixed points of nonnegative neural networks","created":"2021-06-30","cited_by_count":5,"area":null},{"id":"2211.17158","title":"Proximal Residual Flows for Bayesian Inverse Problems","created":"2022-11-30","cited_by_count":5,"area":null},{"id":"2110.07202","title":"Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution","created":"2021-10-14","cited_by_count":4,"area":null},{"id":"2108.13993","title":"Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2603.10562","title":"Quantization Robustness of Monotone Operator Equilibrium Networks","created":"2026-03-11","cited_by_count":3,"area":null},{"id":"2104.10329","title":"Deep Transform and Metric Learning Networks","created":"2021-04-21","cited_by_count":2,"area":null},{"id":"2206.04471","title":"Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective","created":"2022-06-09","cited_by_count":2,"area":null},{"id":"2512.04745","title":"An Energy-Based Mechanism for Compositional Behavior","created":"2025-12-04","cited_by_count":2,"area":null},{"id":"2103.05804","title":"Reframing Neural Networks: Deep Structure in Overcomplete Representations","created":"2021-03-10","cited_by_count":1,"area":null},{"id":"2412.17888","title":"Stability Bounds for the Unfolded Forward-Backward Algorithm","created":"2024-12-23","cited_by_count":1,"area":null},{"id":"2509.13793","title":"Circuit realization and hardware linearization of monotone operator equilibrium networks","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2103.16337","title":"Variational models for signal processing with Graph Neural Networks","created":"2021-03-30","cited_by_count":0,"area":null},{"id":"2302.10895","title":"CQnet: convex-geometric interpretation and constraining neural-network trajectories","created":"2023-02-09","cited_by_count":0,"area":null},{"id":"2408.08742","title":"A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers","created":"2024-08-16","cited_by_count":0,"area":null},{"id":"2601.04120","title":"A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems","created":"2026-01-07","cited_by_count":0,"area":null},{"id":"2606.08374","title":"Predictive Coding with Bayesian Priors via Proximal Gradients","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2610.20120","title":"A Bregman Proximal Viewpoint on Neural Operators","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}